Biology subjects

Kalweit, M.

Publications and source records attributed to Kalweit, M..

3 recordsLinked to original sources

bioRxiv2023

Detection of disease-specific signatures in B cell repertoires of lymphomas using machine learning

The classification of B cell lymphomas - mainly based on light microscopy evaluation by a pathologist - requires many years of training. Since the B cell receptor (BCR) of the lymphoma clonotype and the microenvironmental immune architecture are important features discriminating different lymphoma subsets, we asked whether BCR repertoire next-generation sequencing (NGS) of lymphoma-infiltrated tissues in conjunction with machine learning algorithms could have diagnostic utility in the subclassification of these cancers. We trained a random forest and a linear classifier via logistic regression based on patterns of clonal distribution, VDJ gene usage and physico-chemical properties of the top-n most frequently represented clonotypes in the BCR repertoires of 620 paradigmatic lymphomas - nodular lymphocyte predominant B cell lymphoma (NLPBL), diffuse large B cell lymphoma (DLBCL) and chronic lymphocytic leukemia (CLL) - as well as 291 control tissues. With regard to DLBCL and CLL, the models demonstrated optimal performance when utilizing only the most prevalent clonotype for classification, while in NLPBL - that has a dominant background of non-malignant bystander cells - a broader array of clonotypes enhanced model accuracy. Surprisingly, the straightforward logistic regression model performed best in this seemingly complex classification problem, suggesting linear separability in our chosen dimensions. It achieved a weighted F1-score of 0.84 on a test cohort including 125 cases from all three lymphoma entities and 58 healthy individuals. Together, we provide proof-of-concept that at least the 3 studied lymphoma entities can be differentiated from each other using BCR repertoire NGS on lymphoma-infiltrated tissues by a trained machine learning model. Author SummaryLymphoma, a complex group of malignant blood cancers, poses a significant diagnostic challenge due to its diverse subtypes. Yet, precise classification is crucial for tailored treatment. In our research, we developed a machine learning algorithm and conducted comprehensive validation to discern distinct B cell lymphoma subtypes. We therefore leveraged B cell repertoires of lymphoma-infiltrated tissue, as ascertained through next-generation sequencing. Our data offers three key insights: We detail the creation and training of our machine learning algorithm, explaining how we selected features and designed the model. We demonstrate the algorithms diagnostic precision using sequencing data from a test-set of patients. Moreover, through a deep dive into the most distinguishing aspects of our algorithm, we unveil distinctive disease-related patterns present within the malignant B cell and its surrounding environment. This analysis showed that both the malignant lymphoma cell, but also healthy bystander immune cells contribute to the distinctive architecture that characterizes a specific lymphoma subtype. We hope our work will contribute towards creating tools to diagnose lymphoma more easily and accurately ultimately leading to better outcomes for patients with this type of cancer.

bioinformatics↗

bioRxiv2023

Mechanisms of Premotor-Motor Cortex Interactions during Goal Directed Behavior

Deciphering the neural code underlying goal-directed behavior is a long-term mission in neuroscience1,2. Neurons exhibiting preparation and movement-related activity are intermingled in the premotor and motor cortices3,4, thus concealing the neural code of planned movements. We employed a combination of electrophysiology, pathway-specific optogenetics, phototagging, and inverse reinforcement learning (RL) to elucidate the role of defined neuronal subpopulations in the rat rostral and caudal forelimb areas (RFA and CFA), which correspond to the premotor and motor cortical areas. The inverse RL enabled the functional dissection of spatially intermingled neuronal subpopulations, complementing our pathway-specific optogenetic manipulations and unveiling differential functions of the preparation and movement subpopulations projecting from RFA to CFA. Our results show that the projecting preparation subpopulation suppresses movements, whereas the projecting movement subpopulation promotes actions. We found the influence of RFA on CFA to be adaptable, with the projection either inhibiting or exciting neurons in the superficial and deep CFA layers, depending on context and task phase. These complex interactions between RFA and CFA likely involve the differential recruitment of inhibitory interneurons in the CFA, which is supported by our electron microscopy analysis of the connectivity between these regions. We provide here unprecedented mechanistic insights into how the premotor and primary motor cortices are functionally and structurally interlinked with the potential to advance neuroprosthetics. Graphical abstractThis study provides mechanistic insights into the interactions between the rostral forelimb area (RFA) and the caudal forelimb area (CFA). Specifically, we provide evidence for a differential impact of RFA on CFA depending on the task phase and the targeted CFA layers. RFA contains at least two spatially intermingled subpopulations - one related to movement preparation and one to movement execution. Both subpopulations project to CFA. Here we investigated the impact of these two subpopulations on the activity of the local CFA circuit as well as on the behavior in different contexts. When rats were not involved in a task, the effect of RFA was mainly excitatory in the deep CFA layers, while the superficial layers remained unaffected. This can be interpreted as a non-selective activation of the deep CFA neurons enabling a variety of spontaneous movements. During the preparation phase before a movement, the RFA had an opposite impact on the superficial and deep layers: while the superficial CFA layers were excited by RFA input, the deeper layers were mostly inhibited, minimizing movements and enabling continued holding of a lever. During the movement phase, the inhibitory effect on neurons in the deep CFA layers was counterbalanced by excitation, thus enabling a selected conduction of movements. The opposing effects during preparation and movement phase on CFA deep layers were correlated with increased firing rates of the RFA preparation and movement subpopulations, respectively, making it likely that the inhibition resulted from increased activities of these subpopulation specifically. With an electron microcopy approach we demonstrate that inhibitory and excitatory CFA neurons are directly targeted by RFA, thus providing a mechanism for the bidirectional control of CFA activity. Please note that the depicted impact of RFA on excitatory or inhibitory CFA neurons refers to net effects in this figure, not to the targeting of individual neurons. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/524944v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@4de2d5org.highwire.dtl.DTLVardef@167164forg.highwire.dtl.DTLVardef@e9d298org.highwire.dtl.DTLVardef@101320b_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2022

Process-guidance improves predictive performance of neural networks for carbon turnover in ecosystems

Despite deep-learning being state-of-the-art for data-driven model predictions, it has not yet found frequent application in ecology. Given the low sample size typical in many environmental research fields, the default choice for the modelling of ecosystems and its functions remain process-based models. The process understanding coded in these models complements the sparse data and neural networks can detect hidden dynamics even in noisy data. Embedding the process model in the neural network adds information to learn from, improving interpretability and predictive performance of the combined model towards the data-only neural networks and the mechanism-only process model. At the example of carbon fluxes in forest ecosystems, we compare different approaches of guiding a neural network towards process model theory. Evaluation of the results under four classical prediction scenarios supports decision-making on the appropriate choice of a process-guided neural network. O_TEXTBOXSignificance StatementDeep-learning is the state-of-the-art for data-driven model predictions. Given the low sample size typical in many environmental research fields, these approaches can rarely be applied. When data are complemented by process understanding, as coded in physical or empirical models, both predictions and generalisations can be substantially improved over both data-only neural networks and mechanism-only process models. Comparing different approaches of such process-guidance helps decide on how to best combine process models and neural networks. C_TEXTBOX

ecology↗